TELKOMNIKA Telecommunication, Computing, Electronics and Control
Deep learning model for thorax diseases detection
Dublin Core
Title
TELKOMNIKA Telecommunication, Computing, Electronics and Control
Deep learning model for thorax diseases detection
Deep learning model for thorax diseases detection
Subject
Chest radiography, Deep learning, Internet of things,ResNet-50, Thorax diseases
Description
Despite the availability of radiology devices in some health care centers,
thorax diseases are considered as one of the most common health problems, especially in rural areas. By exploiting the power of the Internet of things and specific platforms to analyze a large volume of medical data, the health of a patient could be improved earlier. In this paper, the proposed model is based on pre-trained ResNet-50 for diagnosing thorax diseases. Chest x-ray images are cropped to extract the rib cage part from the chest radiographs. ResNet-50 was re-train on Chest x-ray14 dataset where a chest radiograph images are inserted into the model to determine if the person is healthy or not. In the case of an unhealthy patient, the model can classify the disease into one of the fourteen chest diseases. The results show the ability of ResNet-50 in achieving impressive performance in classifying thorax diseases.
thorax diseases are considered as one of the most common health problems, especially in rural areas. By exploiting the power of the Internet of things and specific platforms to analyze a large volume of medical data, the health of a patient could be improved earlier. In this paper, the proposed model is based on pre-trained ResNet-50 for diagnosing thorax diseases. Chest x-ray images are cropped to extract the rib cage part from the chest radiographs. ResNet-50 was re-train on Chest x-ray14 dataset where a chest radiograph images are inserted into the model to determine if the person is healthy or not. In the case of an unhealthy patient, the model can classify the disease into one of the fourteen chest diseases. The results show the ability of ResNet-50 in achieving impressive performance in classifying thorax diseases.
Creator
Ghada A. Shadeed, Mohammed A. Tawfeeq, Sawsan M. Mahmoud
Source
DOI: 10.12928/TELKOMNIKA.v18i1.12997
Publisher
Universitas Ahmad Dahlan
Date
February 2020
Contributor
Sri Wahyuni
Rights
ISSN: 1693-6930
Relation
http://journal.uad.ac.id/index.php/TELKOMNIKA
Format
PDF
Language
English
Type
Text
Coverage
TELKOMNIKA Telecommunication, Computing, Electronics and Control
Files
Collection
Citation
Ghada A. Shadeed, Mohammed A. Tawfeeq, Sawsan M. Mahmoud, “TELKOMNIKA Telecommunication, Computing, Electronics and Control
Deep learning model for thorax diseases detection,” Repository Horizon University Indonesia, accessed April 4, 2025, https://repository.horizon.ac.id/items/show/3600.
Deep learning model for thorax diseases detection,” Repository Horizon University Indonesia, accessed April 4, 2025, https://repository.horizon.ac.id/items/show/3600.